Safe and Scalable Web Agent Learning via Recreated Websites

• URL: http://arxiv.org/abs/2603.10505v1
• Date: Wed, 11 Mar 2026 07:58:53 GMT
• Title: Safe and Scalable Web Agent Learning via Recreated Websites
• Abstract summary: Training web agents is fundamentally limited by environments they learn from.<n>We propose a framework that treats language models as environment creators, automatically cloning real-world websites into verifiable synthetic environments.
• Score: 27.277983097111335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training autonomous web agents is fundamentally limited by the environments they learn from: real-world websites are unsafe to explore, hard to reset, and rarely provide verifiable feedback. We propose VeriEnv, a framework that treats language models as environment creators, automatically cloning real-world websites into fully executable, verifiable synthetic environments. By exposing controlled internal access via a Python SDK, VeriEnv enables agents to self-generate tasks with deterministic, programmatically verifiable rewards, eliminating reliance on heuristic or LLM-based judges. This design decouples agent learning from unsafe real-world interaction while enabling scalable self-evolution through environment expansion. Through experiments on web agent benchmarks, we show that agents trained with VeriEnv generalize to unseen websites, achieve site-specific mastery through self-evolving training, and benefit from scaling the number of training environments. Code and resources will be released at https://github.com/kyle8581/VeriEnv upon acceptance.

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